"""simple docstring"""


import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / 'utils'))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
	from test_module.custom_tokenization_fast import CustomTokenizerFast






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										# A mock response for an HTTP head request to emulate server down
										lowerCamelCase__   =mock.Mock()
										lowerCamelCase__   =500
										lowerCamelCase__   ={}
										lowerCamelCase__   =HTTPError
										lowerCamelCase__   ={}

										# Download this model to make sure it's in the cache.
										lowerCamelCase__   =BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )

										# Under the mock environment we get a 500 error when trying to reach the tokenizer.
										with mock.patch("requests.Session.request"      ,					return_value=_lowerCamelCase ) as mock_head:
																lowerCamelCase__   =BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
																# This check we did call the fake head request
																mock_head.assert_called()

				@require_tokenizers
				def 						_a      ( self ):
										# A mock response for an HTTP head request to emulate server down
										lowerCamelCase__   =mock.Mock()
										lowerCamelCase__   =500
										lowerCamelCase__   ={}
										lowerCamelCase__   =HTTPError
										lowerCamelCase__   ={}

										# Download this model to make sure it's in the cache.
										lowerCamelCase__   =GPTaTokenizerFast.from_pretrained("gpt2" )

										# Under the mock environment we get a 500 error when trying to reach the tokenizer.
										with mock.patch("requests.Session.request"      ,					return_value=_lowerCamelCase ) as mock_head:
																lowerCamelCase__   =GPTaTokenizerFast.from_pretrained("gpt2" )
																# This check we did call the fake head request
																mock_head.assert_called()

				def 						_a      ( self ):
										# This test is for deprecated behavior and can be removed in v5
										try:
																lowerCamelCase__   =tempfile.mktemp()
																with open(_lowerCamelCase      ,					"wb" ) as f:
																						http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model"      ,					_lowerCamelCase )

																lowerCamelCase__   =AlbertTokenizer.from_pretrained(_lowerCamelCase )
										finally:
																os.remove(_lowerCamelCase )

										# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
										# the current folder and have the right name.
										if os.path.isfile("tokenizer.json" ):
																# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
																return
										try:
																with open("tokenizer.json"      ,					"wb" ) as f:
																						http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json"      ,					_lowerCamelCase )
																lowerCamelCase__   =AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
																# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
																self.assertEqual(tokenizer.vocab_size      ,					1000 )
																# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

										finally:
																os.remove("tokenizer.json" )






				def 						_a      ( self ):
										# This test is for deprecated behavior and can be removed in v5
										lowerCamelCase__   =AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )






@is_staging_test
class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  Tuple    				=			['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']

				@classmethod
				def 						_a      ( cls ):
										lowerCamelCase__   =TOKEN
										HfFolder.save_token(_lowerCamelCase )

				@classmethod
				def 						_a      ( cls ):
										try:
																delete_repo(token=cls._token      ,					repo_id="test-tokenizer" )
										except HTTPError:
																pass

										try:
																delete_repo(token=cls._token      ,					repo_id="valid_org/test-tokenizer-org" )
										except HTTPError:
																pass

										try:
																delete_repo(token=cls._token      ,					repo_id="test-dynamic-tokenizer" )
										except HTTPError:
																pass

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmp_dir:
																lowerCamelCase__   =os.path.join(_lowerCamelCase      ,					"vocab.txt" )
																with open(_lowerCamelCase      ,					"w"      ,					encoding="utf-8" ) as vocab_writer:
																						vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
																lowerCamelCase__   =BertTokenizer(_lowerCamelCase )

										tokenizer.push_to_hub("test-tokenizer"      ,					use_auth_token=self._token )
										lowerCamelCase__   =BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer''' )
										self.assertDictEqual(new_tokenizer.vocab      ,					tokenizer.vocab )

										# Reset repo
										delete_repo(token=self._token      ,					repo_id="test-tokenizer" )

										# Push to hub via save_pretrained
										with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(_lowerCamelCase      ,					repo_id="test-tokenizer"      ,					push_to_hub=_lowerCamelCase      ,					use_auth_token=self._token )

										lowerCamelCase__   =BertTokenizer.from_pretrained(F'''{USER}/test-tokenizer''' )
										self.assertDictEqual(new_tokenizer.vocab      ,					tokenizer.vocab )

				def 						_a      ( self ):
										with tempfile.TemporaryDirectory() as tmp_dir:
																lowerCamelCase__   =os.path.join(_lowerCamelCase      ,					"vocab.txt" )
																with open(_lowerCamelCase      ,					"w"      ,					encoding="utf-8" ) as vocab_writer:
																						vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
																lowerCamelCase__   =BertTokenizer(_lowerCamelCase )

										tokenizer.push_to_hub("valid_org/test-tokenizer-org"      ,					use_auth_token=self._token )
										lowerCamelCase__   =BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
										self.assertDictEqual(new_tokenizer.vocab      ,					tokenizer.vocab )

										# Reset repo
										delete_repo(token=self._token      ,					repo_id="valid_org/test-tokenizer-org" )

										# Push to hub via save_pretrained
										with tempfile.TemporaryDirectory() as tmp_dir:
																tokenizer.save_pretrained(
																    _lowerCamelCase      ,					repo_id="valid_org/test-tokenizer-org"      ,					push_to_hub=_lowerCamelCase      ,					use_auth_token=self._token )

										lowerCamelCase__   =BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
										self.assertDictEqual(new_tokenizer.vocab      ,					tokenizer.vocab )






				@require_tokenizers
				def 						_a      ( self ):
										CustomTokenizer.register_for_auto_class()
										with tempfile.TemporaryDirectory() as tmp_dir:
																lowerCamelCase__   =os.path.join(_lowerCamelCase      ,					"vocab.txt" )
																with open(_lowerCamelCase      ,					"w"      ,					encoding="utf-8" ) as vocab_writer:
																						vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
																lowerCamelCase__   =CustomTokenizer(_lowerCamelCase )

										# No fast custom tokenizer
										tokenizer.push_to_hub("test-dynamic-tokenizer"      ,					use_auth_token=self._token )

										lowerCamelCase__   =AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''      ,					trust_remote_code=_lowerCamelCase )
										# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
										self.assertEqual(tokenizer.__class__.__name__      ,					"CustomTokenizer" )

										# Fast and slow custom tokenizer
										CustomTokenizerFast.register_for_auto_class()
										with tempfile.TemporaryDirectory() as tmp_dir:
																lowerCamelCase__   =os.path.join(_lowerCamelCase      ,					"vocab.txt" )
																with open(_lowerCamelCase      ,					"w"      ,					encoding="utf-8" ) as vocab_writer:
																						vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )

																lowerCamelCase__   =BertTokenizerFast.from_pretrained(_lowerCamelCase )
																bert_tokenizer.save_pretrained(_lowerCamelCase )
																lowerCamelCase__   =CustomTokenizerFast.from_pretrained(_lowerCamelCase )

										tokenizer.push_to_hub("test-dynamic-tokenizer"      ,					use_auth_token=self._token )

										lowerCamelCase__   =AutoTokenizer.from_pretrained(F'''{USER}/test-dynamic-tokenizer'''      ,					trust_remote_code=_lowerCamelCase )
										# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
										self.assertEqual(tokenizer.__class__.__name__      ,					"CustomTokenizerFast" )
										lowerCamelCase__   =AutoTokenizer.from_pretrained(
										    F'''{USER}/test-dynamic-tokenizer'''      ,					use_fast=_lowerCamelCase      ,					trust_remote_code=_lowerCamelCase )
										# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
										self.assertEqual(tokenizer.__class__.__name__      ,					"CustomTokenizer" )






class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										trie.add("Hello 友達" )
										self.assertEqual(trie.data      ,					{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
										trie.add("Hello" )
										trie.data
										self.assertEqual(trie.data      ,					{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										self.assertEqual(trie.split("[CLS] This is a extra_id_100" )      ,					["[CLS] This is a extra_id_100"] )
										trie.add("[CLS]" )
										trie.add("extra_id_1" )
										trie.add("extra_id_100" )
										self.assertEqual(trie.split("[CLS] This is a extra_id_100" )      ,					["[CLS]", " This is a ", "extra_id_100"] )

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										trie.add("A" )
										self.assertEqual(trie.split("ABC" )      ,					["A", "BC"] )
										self.assertEqual(trie.split("BCA" )      ,					["BC", "A"] )

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										trie.add("TOKEN]" )
										trie.add("[SPECIAL_TOKEN]" )
										self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" )      ,					["This is something ", "[SPECIAL_TOKEN]"] )

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										trie.add("A" )
										trie.add("P" )
										trie.add("[SPECIAL_TOKEN]" )
										self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" )      ,					["This is something ", "[SPECIAL_TOKEN]"] )

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										trie.add("AB" )
										trie.add("B" )
										trie.add("C" )
										self.assertEqual(trie.split("ABC" )      ,					["AB", "C"] )

				def 						_a      ( self ):
										lowerCamelCase__   =Trie()
										trie.add("ABC" )
										trie.add("B" )
										trie.add("CD" )
										self.assertEqual(trie.split("ABCD" )      ,					["ABC", "D"] )






				def 						_a      ( self ):
										# Even if the offsets are wrong, we necessarily output correct string
										# parts.
										lowerCamelCase__   =Trie()
										lowerCamelCase__   =trie.cut_text("ABC"      ,					[0, 0, 2, 1, 2, 3] )
										self.assertEqual(_lowerCamelCase      ,					["AB", "C"] )



"""simple docstring"""


import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    PNDMScheduler,
    StableDiffusionLDMaDPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS


enable_full_determinism()






class   __UpperCAmelCase		(      unittest.TestCase      ):
				A__ :  List[Any]    				=			StableDiffusionLDMaDPipeline
				A__ :  List[str]    				=			TEXT_TO_IMAGE_PARAMS
				A__ :  str    				=			TEXT_TO_IMAGE_BATCH_PARAMS
				A__ :  Tuple    				=			TEXT_TO_IMAGE_IMAGE_PARAMS

				def 						_a      ( self ):
										torch.manual_seed(0 )
										lowerCamelCase__   =UNetaDConditionModel(
										    block_out_channels=(32, 64)      ,					layers_per_block=2      ,					sample_size=32      ,					in_channels=4      ,					out_channels=4      ,					down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")      ,					up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")      ,					cross_attention_dim=32      ,					)
										lowerCamelCase__   =DDIMScheduler(
										    beta_start=0.0_0_0_8_5      ,					beta_end=0.0_1_2      ,					beta_schedule="scaled_linear"      ,					clip_sample=_lowerCamelCase      ,					set_alpha_to_one=_lowerCamelCase      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =AutoencoderKL(
										    block_out_channels=[32, 64]      ,					in_channels=6      ,					out_channels=6      ,					down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]      ,					up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]      ,					latent_channels=4      ,					)
										torch.manual_seed(0 )
										lowerCamelCase__   =CLIPTextConfig(
										    bos_token_id=0      ,					eos_token_id=2      ,					hidden_size=32      ,					intermediate_size=37      ,					layer_norm_eps=1E-05      ,					num_attention_heads=4      ,					num_hidden_layers=5      ,					pad_token_id=1      ,					vocab_size=1000      ,					)
										lowerCamelCase__   =CLIPTextModel(_lowerCamelCase )
										lowerCamelCase__   =CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )

										lowerCamelCase__   ={
										    "unet": unet,
										    "scheduler": scheduler,
										    "vae": vae,
										    "text_encoder": text_encoder,
										    "tokenizer": tokenizer,
										    "safety_checker": None,
										    "feature_extractor": None,
										}
										return components

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase=0 ):
										if str(_lowerCamelCase ).startswith("mps" ):
																lowerCamelCase__   =torch.manual_seed(_lowerCamelCase )
										else:
																lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "A painting of a squirrel eating a burger",
										    "generator": generator,
										    "num_inference_steps": 2,
										    "guidance_scale": 6.0,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator

										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_3_3_8_1_7_6, 0.7_0_2_4_7, 0.7_4_2_0_3_1_9_3, 0.5_1_6_4_3_6_0_4, 0.5_8_2_5_6_7_9_3, 0.6_0_9_3_2_1_3_6, 0.4_1_8_1_0_9_5, 0.4_8_3_5_5_8_7_7, 0.4_6_5_3_5_2_6_2] )
										lowerCamelCase__   =np.array([1_0_3.4_6_7_2_7, 8_5.8_1_2_0_0_4, 8_7.8_4_9_2_3_6] )

										assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1E-2

				def 						_a      ( self ):
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs["prompt"]]

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   =3 * [inputs.pop("prompt" )]

										lowerCamelCase__   =ldmad_pipe.tokenizer(
										    _lowerCamelCase      ,					padding="max_length"      ,					max_length=ldmad_pipe.tokenizer.model_max_length      ,					truncation=_lowerCamelCase      ,					return_tensors="pt"      ,					)
										lowerCamelCase__   =text_inputs["input_ids"].to(_lowerCamelCase )

										lowerCamelCase__   =ldmad_pipe.text_encoder(_lowerCamelCase )[0]

										lowerCamelCase__   =prompt_embeds

										# forward
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb_slice_a[0, -3:, -3:, -1]
										lowerCamelCase__   =depth_slice_a[0, -3:, -1]

										assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1E-4
										assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1E-4






				def 						_a      ( self ):
										lowerCamelCase__   ="cpu"  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase__   =self.get_dummy_components()
										lowerCamelCase__   =PNDMScheduler(skip_prk_steps=_lowerCamelCase )
										lowerCamelCase__   =StableDiffusionLDMaDPipeline(**_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_dummy_inputs(_lowerCamelCase )
										lowerCamelCase__   ="french fries"
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase      ,					negative_prompt=_lowerCamelCase )

										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1]
										lowerCamelCase__   =depth[0, -3:, -1]

										assert rgb.shape == (1, 64, 64, 3)
										assert depth.shape == (1, 64, 64)

										lowerCamelCase__   =np.array(
										    [0.3_7_0_4_4, 0.7_1_8_1_1_5_0_3, 0.7_2_2_3_2_5_1, 0.4_8_6_0_3_6_7_5, 0.5_6_3_8_3_9_1, 0.6_3_6_4_9_4_8, 0.4_2_8_3_3_7_0_4, 0.4_9_0_1_3_1_5, 0.4_7_9_2_6_2_1_7] )
										lowerCamelCase__   =np.array([1_0_7.8_4_7_3_8, 8_4.6_2_8_0_2, 8_9.9_6_2_1_3_5] )
										assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1E-2
										assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1E-2






@slow
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 3,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" )
										lowerCamelCase__   =ldmad_pipe.to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth
										lowerCamelCase__   =rgb[0, -3:, -3:, -1].flatten()
										lowerCamelCase__   =rgb[0, -3:, -1].flatten()

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512)

										lowerCamelCase__   =np.array(
										    [0.5_3_8_0_5_4_6_5, 0.5_6_7_0_7_3_0_5, 0.5_4_8_6_5_1_5, 0.5_7_0_1_2_2_3_6, 0.5_8_1_4_5_1_1, 0.5_6_2_5_3_4_8_7, 0.5_4_8_4_3_0_1_4, 0.5_5_0_9_2_2_6_3, 0.6_4_5_9_7_0_6] )
										lowerCamelCase__   =np.array(
										    [0.9_2_6_3_7_8_1, 0.6_6_7_8_6_7_2, 0.5_4_8_6_5_1_5, 0.9_2_2_0_2_1_4_5, 0.6_7_8_3_1_1_3_5, 0.5_6_2_5_3_4_8_7, 0.9_2_4_1_6_9_4, 0.7_5_5_1_4_7_8, 0.6_4_5_9_7_0_6] )
										assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3E-3
										assert np.abs(depth_slice - expected_slice_depth ).max() < 3E-3






@nightly
@require_torch_gpu
class   __UpperCAmelCase		(      unittest.TestCase      ):

				def 						_a      ( self ):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()

				def 						_a      ( self      ,					_lowerCamelCase      ,					_lowerCamelCase="cpu"      ,					_lowerCamelCase=torch.floataa      ,					_lowerCamelCase=0 ):
										lowerCamelCase__   =torch.Generator(device=_lowerCamelCase ).manual_seed(_lowerCamelCase )
										lowerCamelCase__   =np.random.RandomState(_lowerCamelCase ).standard_normal((1, 4, 64, 64) )
										lowerCamelCase__   =torch.from_numpy(_lowerCamelCase ).to(device=_lowerCamelCase      ,					dtype=_lowerCamelCase )
										lowerCamelCase__   ={
										    "prompt": "a photograph of an astronaut riding a horse",
										    "latents": latents,
										    "generator": generator,
										    "num_inference_steps": 50,
										    "guidance_scale": 7.5,
										    "output_type": "numpy",
										}
										return inputs

				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_9_5_5_8_6
										lowerCamelCase__   =0.3_3_7_9_5_5_1_5
										lowerCamelCase__   =1_1_2.4_8_5_1_8
										lowerCamelCase__   =9_8.4_8_9_7_4_6
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3






				def 						_a      ( self ):
										lowerCamelCase__   =StableDiffusionLDMaDPipeline.from_pretrained("Intel/ldm3d-4c" ).to(_lowerCamelCase )
										ldmad_pipe.set_progress_bar_config(disable=_lowerCamelCase )

										lowerCamelCase__   =self.get_inputs(_lowerCamelCase )
										lowerCamelCase__   =ldmad_pipe(**_lowerCamelCase )
										lowerCamelCase__   , lowerCamelCase__   =output.rgb, output.depth

										lowerCamelCase__   =0.4_1_9_4_1_2_7
										lowerCamelCase__   =0.3_5_3_7_5_5_8_6
										lowerCamelCase__   =0.5_6_3_8_5_0_2
										lowerCamelCase__   =0.3_4_6_8_6_1_0_3

										assert rgb.shape == (1, 512, 512, 3)
										assert depth.shape == (1, 512, 512, 1)
										assert np.abs(expected_rgb_mean - rgb.mean() ) < 1E-3
										assert np.abs(expected_rgb_std - rgb.std() ) < 1E-3
										assert np.abs(expected_depth_mean - depth.mean() ) < 1E-3
										assert np.abs(expected_depth_std - depth.std() ) < 1E-3


